Aiming at the existing target detection algorithms，which suffer from problems such as low accuracy and incomplete detection of small targets in traffic signs，a traffic sign detection algorithm based on an improved YOLO11n was proposed. Initially，a convolution and attention fusion module （CAFM） was integrated into the backbone network of YOLO11n to comprehensively model global and local image features through the combined operation of convolutional layers and attention mechanisms，thereby improving small target feature representation. Subsequently，the lightweight dynamic up-sampling algorithm DySample was introduced to optimize feature sampling by concentrating sampling points on target regions，effectively mitigating feature information loss and enhancing small target perception. Then，a novel C3k2_SC module was designed by incorporating selective convolution （SCConv） into the C3k2 structure，which improved detection accuracy while reducing model parameter complexity. Finally，an additional small target detection layer was added to preserve shallow-level detailed information and strengthen the fusion of deep and shallow semantic features，thereby resolving incomplete detection of small targets. The experimental results demonstrate that precision，recall，and mean average precision rates of 82.0%，77.5%，and 84.6% are achieved by the improved algorithm on the TT100K dataset，representing improvements of 3.8，9.5，and 8.5 percentage points compared to the original YOLO11n model，proving the effectiveness of the improvement.